Recognition of weed in a natural environment

ABSTRACT

A method ( 100 ) for recognizing weed in a natural environment may be provided. A digital image of weed in an early development stage is captured ( 102 ) among cultivated crop. Areas with a predefined color and texture specification are contoured ( 104 ) defining a boundary contour. The weed is displayed ( 106 ) together with a frame and a quality indicator. Moreover, the digital image is stored ( 110 ) only if a predefined quality criterion is met. Then, unnecessary color information may be reset ( 112 ), the digital image is sent ( 116 ) for a further examination, and a weed name of the weed of the captured image and a related probability value indicative of a probability of a match between the weed name and the weed of the captured digital image together image is received ( 118 ).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage application (under 35 U.S.C. § 371)of PCT/EP2017/060750 filed May 5, 2017, which claims benefit of EuropeanApplication No. 16169416.1, filed May 12, 2016, both of which areincorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The invention relates generally to weed recognition, and morespecifically to a method for recognizing a type of weed in a naturalenvironment. The invention relates further to a recognition system for arecognition of a type of weed in a natural environment, a weed controlmethod and a computer program product.

BACKGROUND

Farming is currently undergoing a new transition. After anindustrialization of many farming processes and a series of automationinitiatives, the introduction of high-tech tools in the farming processcontinues. The phenomenon of the Internet of Things (IoT) does not stopat the boundaries of agricultural fields. On the other side, farmers aremore than interested in increasing the yield of harvesting. However, theyield is also a function of pest and/or weed within the fields.Consequently, farmers have used herbicides in order to fight weed. Sucha treatment normally involves high costs. Therefore, farmers want to beon the save side when a decision is being made to use specificherbicides against specific weed. So far, farmers need to rely on theirexperience and their individual knowledge to identify weed when applyingherbicides. However, choosing the right herbicides in the correct amountmay be instrumental in order to save money and further protect theenvironment.

Therefore, there is a need for technical help for identifying weed in afarm field without the manual process of picking individual leafs ofpotential weed in the farm field and trying to identify the type of weedin a back-office using some literature. It is therefore an objective ofthe current application to provide a method and a system for identifyingweed in a natural environment among crop.

SUMMARY

This need may be addressed by a method for recognizing of a type of weedin a natural environment, a recognition system for recognition of a typeof weed in a natural environment, a weed control method and a computerprogram product, according to the independent claims.

According to one aspect of the present invention, a method forrecognizing a type of weed in a natural environment may be provided. Themethod may comprise capturing a digital image of weed among cultivatedcrop in the natural environment, wherein the weed is in an earlydevelopment stage, and contouring areas with a predefined color andtexture specification in an RGB color model within the digital imagebuilding at least one contoured area comprising pixels relating to theweed within a boundary contour.

The digital image may be displayed together with a smallest of apredefined convex frame surrounding the contoured areas. Additionally,an indicator value may be displayed together with the digital image andthe smallest of the predefined convex frame. The indicator may beindicative of a predefined quality criterion of the digital image basedon determining that the smallest of the predefined convex frame covers apredefined minimum area of an available display area, and measuring apositive output value of a focus detector in respect to the contouredareas. The positive output value is related to an image sharpness of anarea of the digital image within the contoured area.

The digital image may be stored only if the indicator value indicatesthat the digital image meets the predefined quality criterion, and thecolor information of the digital image outside the redefined convexframe may be reset. Additionally, the digital image may be sent—inparticular to a server—for further examination, and a weed name of theweed of the captured image and a related probability value indicative ofa probability of a match between the weed name and the weed of thecaptured digital image may be received back.

According to another aspect of the present invention, a recognitionsystem for recognition of a type of weed in a natural environment may beprovided. The recognition system may comprise a digital camera—inparticular in a smartphone—adapted for capturing a digital image of weedamong cultivated crop in the natural environment, wherein the weed is inan early development stage, and a contouring module adapted forcontouring areas with a predefined color and texture specification in anRGB color model within the digital image building at least one contouredarea comprising pixels relating to the weed within a boundary.

Furthermore, the recognition system may comprise a display adapted fordisplaying the digital image together with a smallest of a predefinedconvex frame surrounding the contoured areas, wherein the display isalso adapted for displaying an indicator value together with the digitalimage and the smallest of the predefined convex frame. The indicator isindicating of a predefined quality criterion of the digital image basedon a determination that the smallest of the predefined convex framecovers a predefined minimum area of an available display area, and ameasurement of a positive output value of a focus detector in respect tothe contoured area. The positive output value is related to an imagesharpness of an area of the digital image relating to the contouredarea.

An additional storage as part of the recognition system may be adaptedfor storing the digital image only if the indicator value indicates thatthe digital image meets the predefined quality criterion. A resettingunit may be adapted for resetting color information of the digital imageoutside the predefined convex frame.

Moreover, the recognition system may comprise a sender module adaptedfor sending the digital image for further examination, and a receiveradapted for receiving a weed name of the weed of the captured image anda related probability value indicative of a probability of a matchbetween the weed name and the weed of the captured digital image.

It may be noted that the recognition system may be implemented as partof a smartphone and that the method for recognizing a type of weed in anatural environment may be implemented using a smartphone.

Furthermore, embodiments may take the form of a related computer programproduct, accessible from a computer-usable or computer-readable mediumproviding program code for use, by or in connection with a computer orany instruction execution system. For the purpose of this description, acomputer-usable or computer-readable medium may be any apparatus thatmay contain means for storing, communicating, propagating ortransporting the program for use, by or in a connection with theinstruction execution system, apparatus, or device.

The proposed method for recognizing of a type of weed in a naturalenvironment may offer a couple of advantages and technical effects:

Automatically detecting a type of weed and determining the correct weedname is of high value for farmers in the field. Weed in a crop field maychange from season to season, from weather condition to weathercondition and from one year to another year. In addition, the appearanceof weed may change based on environmental conditions such that alsospecialists may not be able to identify a type of weed in a reliablemanner. Especially during early development stages, it may be difficultto recognize small differences in the appearance of a specific weed.Using a camera, e.g., from a smartphone, taking a picture, performing apreanalysis within the smartphone and/or sending the picture to ananalysis system may provide tremendous value for the farmer. He maydecide instantly which type of herbicide to use in order to fight thedeveloping weed among his crop. It may also help to apply the herbicideonly at those areas where the weed has grown.

The task of capturing an image of a potential weed may also be performedby noneducated farmers, support personnel or temporary staff without anyexperience in recognizing weed. This may save valuable time and money inthe farming process. The combination of a sort of pre-processing acaptured image of weed in a smartphone and reducing the imageinformation to a useful minimum, together with the usage of a highperformance, highly sophisticated weed analysis system in the form of aneural network system may allow to determine the name of the weed and tosend it back to the smartphone of the person having captured the weedimage more or less instantly. The usage of metadata as input values forthe neural network system generating the weed name together with theprobability of one or more weed types and sending this informationallows for sophisticated interaction between the farmer and the backendsystem. It also allows determining that weed name with a highprobability and to advise the farmer how to proceed. Moreover, it may bepossible to implement the neural network technology in high-performancemulti-core bases smartphones or dedicated weed analysis systems.

Moreover, this whole process of capturing the image of the weed andreceiving back the weed name—with a probability rating—may be performedwithin a very short amount of time, e.g., seconds. No special equipment,like special cameras, in the field may be required because smartphonesare omnipresent today. Thus, the costs for the recognition process mayalso be kept comparably low in comparison to traditional technologies.

Additionally, the person taking the picture may receive help and hintsin order to capture the image in a most adequate fashion in order toincrease the probability for a correct determination and recognition ofthe weed at hand. The user of the image capturing device may getinstantaneous advice during the image capturing process whether thequality of the image is expected to be high enough for a thoroughanalysis. The user may then adjust his image capturing behavior in orderto increase the quality of the captured weed image.

Furthermore, the proposed method and system allows a recognition anddetermination of weed in a natural environment and not only in a glasshouse on artificial conditions. This may prove to be an invaluableadvantage to farmers working in the field because everything can beachieved life and under the realistic daily working conditions.Recognizing weed in a natural environment has proven to be much harderthan under glass house conditions because the number of variables issignificantly higher. In a glass house there may be constant light, nodirect sunshine or single light source related strong shadowing, nolight angle variations related to time of the day, leading to changingand unpredictable reflections and color appearance no cloud, no fog orhaze or varying soil conditions, no varying wetness conditions, no windinduced plant movement, no insect related leaf damage, just to name afew parameters why a recognition in a natural environment cannot becompared with a weed recognition in a glass house. Thus, all of theabove-mentioned circumstances vary under natural conditions, which mayrepresent a significant difference to partially known technologies forimage recognition which rely typically on optimal and often artificialenvironmental conditions.

In the following additional embodiments of the proposed method forrecognizing of a type of weed in a natural environment will bedescribed:

According to one preferred embodiment of the method, the predefinedcolor specification may relate to a color range of weed in a naturalenvironment, featuring the complete visible range and in particularfocus on the green color range of wavelength of, e.g., 490 to 575 nm.This may reduce “image noise” from background information also capturedas part of the digital image. Also the background of the captured weedmay comprise single or small groups of green pixels falling into thiswavelength range. The contouring may eliminate this background greeninformation spots. The allowable wavelength range (e.g., 490 to 575 nm)may be stored in a table, a database or another suitable data format.

The texture specification may relate to leaf veins, characteristic formsof leaf segments, specific patterning and color distribution, microhairs on the surface atop of the leaf and on the edge of the leaf. Allof these and additional characteristic textures may not explicitly beparameterized but belong to the “learnt” context of the trainedclassifier correlation function(s).

According to an additionally preferred embodiment of the method, thecontouring the areas with the predefined color and texture specificationmay be performed by determining for every combined color of the digitalimage whether a combination of its color components may match one of aplurality predefined color combinations. Typically, image detectorsfunction using the known RGB color model (red-green-blue) utilizing 3sub-pixels for every complete pixel, wherein one sub-pixel is used forone of the 3 basic colors red, green and blue. The so specified colorspace boundaries with predefined color information and distribution ofintensities within the RGB model of the sub-colors pixel information maybe stored as reference. This way, a fast comparison between each pixelof the digital image and the stored reference color information may beperformed by which a pixel of the captures digital image may be selectedas part of the to-be-contoured area or not.

According to one advantageous embodiment of the method, contouring ofareas with the predefined color specification may be performedadditionally by a determination ofw _(i) =F(p _(i) ,p _(i,j)) wherein  (Eq. 1)

w_(i)=1 or 0 indicating that pixel i may belong to weed or not. This maybe performed in addition to the more straight forward comparison againstallowed color information for a single pixel. F may be a functioncalculating a probability for weed, respectively non-weed based on colorattributes of p_(i) and all of p_(j), p_(i)=pixel i, and p_(i,j)=pixelsj surrounding the pixel i. The number of pixels counted as surroundingpixel i may vary. E.g., only one ring or surrounding pixels may be used;this may be 8 pixels p_(i,j). Moreover, a next ring of pixels p_(i,j)surrounding the first ring may also be considered; this second ring maycomprise additional 16 pixels p_(i,j). Additional pixel rings may beconsidered and different rings may be multiplied with decreasingweighing factors the more distant a pixel ring is from pixel i. This mayenable a more thorough determination whether a pixel of the captureddigital image should count as weed pixel or not.

According to an additionally advantageous embodiment of the method, theindicator may be implemented as a color code of the predefined convexframe. A traffic light style color code may be used. E.g., a red coloredframe may indicate that the quality or size or any other characteristicof the image may not be sufficient. A green frame may indicate that thecaptured image is usable for a further processing. And a yellow framemay indicate that the frame could be better but a further processing maybe allowed but sub-optimal results may be expected. By moving the imagecapturing device or better focusing, the color of the frame may changeimmediately. This way, the user may get direct feedback about thequality of the image. He may have a good chance to improve the imagequality by repositioning or refocusing the capturing device, e.g., theused smartphone. Other image quality criteria may also influence thecolor code of the frame surrounding the contoured area, like, e.g., thetilt angle of the camera.

According to one permissive embodiment of the method, the earlydevelopment stage may be defined by a BBCH code from 10 to 39. Using theBBCH code (the international accepted code from BiologischeBundesanstalt, Bundessortenamt and CHemische Industrie in Germany) mayhelp to achieve comparable results in an early development stage ofweed. Typically, weed with a development stage according to BBCH codebelow 10, e.g., first leafs emerging, may not be recognizable even by anexpert. Weed with a BBCH code larger or equal 40 may be too much grownup to fight it successfully with herbicides. However, also weed with aBBCH code below 10 may be recognized with a lower probability. Increasedspectral analysis of the color information of the contoured area may beinstrumental for achieving this.

According to one optional embodiment of the method, the captureddisplayed image may have a lower resolution than the stored digitalimage. Thus, the displayed image, which also may display the predefinedconvex frame around the captured weed image, may be presented to a userin a video stream-like form. Hence, the displayed image may change inaccordance with a movement of the smartphone. For performance reasons,the image capturing process may be performed with a lower resolutionthan the maximal possible.

Thus, the user may recognize an increasing or decreasing indicator—i.e.,better or worse quality of the digital image—immediately. However, aselected image out of the video-stream may then be re-captured with thefull available resolution of the camera, e.g., if a user may press thecapture button. This may enhance the probability of a correct weedrecognition.

Therefore, it may be an option in one embodiment of the method that thestep of capturing the same digital image at a higher resolution than theinitially captured digital image, may comprise re-performing the stepsof contouring and measuring a positive output value of a focus detectorusing the digital image with the higher resolution. Thus, the steps maybe repeated with the higher resolution image to provide the bestpossible image quality for further processing, i.e., weed recognition.

According to one preferred embodiment of the method, the measuring apositive output value of the focus detector may comprise applying aLaplace filter conversion of a digital greyscale image, which may bederived from the captured digital image, wherein a standard deviation ofgreyscale intensities of pixels lying within an area masked by thecontoured areas. Additionally, a sharpness factor may be derived as asquared standard deviation. The image may have been derived by atransform from an RGB color model to another color model, e.g., HSV(hue, saturation, value), HSL (hue, saturation, lightness/luminance).Using the specific implementation alternative having the squaredstandard deviation, larger intensity differences may be weighted higher,which may improve the quality of the recognition process.

According to a further preferred embodiment of the method, the resettingthe color information also may comprise resetting color information ofareas of the digital image outside the related boundary contour. Thishas the advantage that the required storage requirements for thecaptured digital image may be reduced. This may also be advantageouswhen sending the digital image or a pre-processed version of it. Inaddition to a shape exclusion of color information directly outside thecontoured area also a framing environment of the contour may be excludedfrom the resetting. Thus, a small edge environment—e.g., several pixelswide—at edges of the contoured area may not be reset in respect to colorvalues but keep the original color information. This has also theadvantage of a better recognition of the displayed image by a user ofthe display, e.g., the user of the smartphone. Basically, the resetareas may be reset to a black color value. However, any other defaultcolor—e.g., apart from green—may be used.

According to an optional embodiment, the method may comprise, in casethe weed is a monocotyledon, that the capturing the digital image isperformed with a digital image plane—in particular a capturing planewhich may also be the sensor plane—being parallel to a longitudinalexpansion of the monocotyledon plus or minus a predefined first deltaangle. I.e., ideally such a weed may be captured from a positiondirectly from the side of the monocotyledon, e.g., a grass. Forpractical reasons, it may not be required to hold the camera completelyparallel to the vertical extension of the weed. E.g., plus or minus 10°may be allowed. Alternative angle value may include 5°, 15°, 20°, 25° or30°. Hence, the image plane or sensor plane (without mirrors) may beparallel to the soil. An alternative solution in capturing weed of typemonocotyledon may be to pinch the monocotyledon and lay it more or lessflat on the ground or soil. In this case, the weed may be captured fromabove.

According to another optional embodiment, the method may comprise, incase the weed is a dicotyledon, that the capturing the digital image isperformed with a digital image plane being parallel to the naturalenvironment surrounding the weed plus or minus a predefined second deltaangle, i.e., direct from above the dicotyledon. Also here, for practicalreasons, plus or minus a predefined deviation from the perfectparallelism may be allowed, e.g., + or −10°. The same alternativeangles, as discussed above, may apply here. Typically, the potentialweed may be projected into the middle of an image capturing device,i.e., the image sensor. A good positioning would be recognizable by thedisplay.

According to one additionally advantageous embodiment of the method, afurther examination may comprise applying at least one trainedclassifier correlation function comprising neural network classifiersand sample based identifiers both for weed and single leafs to thecaptured digital image for recognizing the weed, wherein the correlationfunction may have access to names of types of weeds together with aplurality of sets of metadata per weed type. The available smartphone'sperformance is ever increasing. Therefore, neural network classifiersmay be implemented as part of the smartphone, either in software form oras a hardware module. This may eliminate the need to send the digitalimage to a powerful server for further processing, i.e., weedrecognition. Instead, the weed recognition may be performed directly inthe smartphone. Additionally, the found results together with thecaptured digital image and/or the contoured area(s) may be transmittedfrom the smartphone to a server, typically wirelessly.

Other metadata used by the classifier correlation function may includebut not be limited to the country the image has been captured, a datefrom which season information may be derived, a device type of the imagecapturing device (because different camera models may have differentcolor representation models or sensitivities), device orientation,device distance to the weed, data from an accelerometer of the camera,brightness of the environment, soil characteristics (e.g., composition,conductivity), weather history, past results of weed resignation fromthe same environment (same place), comparison with a weed map, pasttreatment with herbicides (in order to determine a potential resistance,and coverage of the soil with weed (e.g., measured or estimated inpercent).

According to a further enhanced method, the method for recognizing atype of weed in a natural environment may be used for weed control,wherein a name of an herbicide is selected out of a database and isreceived together with the weed name and the probability. This mayenable a farmer to select a particular herbicide in order to fight theweed. Advantageous may be an embodiment if the local weather andseasonal conditions may be reflected. This way the farmer may get directaccess to the knowledge base or database of a central knowledge hub andhe may only use as much herbicide as required under the local weatherand seasonal conditions. This may reduce his expenditures for fightingweed on his farmland.

Furthermore, embodiments may take the form of a related computer programproduct, accessible from a computer-usable or computer-readable mediumproviding program code for use, by or in connection with a computer orany instruction execution system, like a smartphone. For the purpose ofthis description, a computer-usable or computer-readable medium may beany apparatus that may contain means for storing, communicating,propagating or transporting the program for use, by or in a connectionwith the instruction execution system, apparatus, or device.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

It should be noted that aspects of the invention are described withreference to different subject-matters. In particular, some embodimentsare described with reference to method type claims whereas otherembodiments have been described with reference to device type claims.However, a skilled person of the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject-matter,also any combination between features relating to differentsubject-matters, in particular, between features of the method typeclaims, and features of the device type claims, is considered as to bedisclosed within this document.

The aspects defined above and further aspects of the present inventionare apparent from the examples of embodiments being describedhereinafter. They are explained with reference to the examples ofembodiments, but to which the invention is not limited.

Preferred embodiments of the invention will be described, by way ofexample only, and with reference to the following drawings:

FIG. 1 shows a block diagram of an embodiment of the inventive methodfor recognizing of a type of weed in a natural environment.

FIG. 2 shows a block diagram of an embodiment for capturing adicotyledon.

FIG. 3 shows a block diagram of an embodiment for capturing amonocotyledon.

FIG. 4 a, b, c show embodiments of steps of an area contouring process.

FIG. 5 a, b illustrates a concept of including further rings of pixelsin the contouring process.

FIG. 6 shows an embodiment of a display with an image of weed and theconvex frame.

FIG. 7 shows a block diagram of an embodiment of the weed recognitionsystem.

FIG. 8 shows an embodiment of a computing system comprising therecognition system.

DETAILED DESCRIPTION

In the context of this description, the following conventions, termsand/or expressions may be used:

The term ‘recognizing’, in particular ‘recognizing a type of weed’ maydenote an automated machine-based determination or recognition processfor a specific type of weed starting from a digital image with a digitalcamera, pre-processing of the digital image, deriving metadata from thedigital image and use these by, e.g., neural network based classifiersfor a probability based analysis of the image data, and finally arecognition of one or more type(s) of weed.

The term ‘weed’ may denote an unwanted plant of any species that canquickly adapt to any environment. Here, the expression weed may also berelated to plant among crop or cultivated or economic plants that areuseful in the sense of harvesting fruits of grown up seed. Weed maydisturb the process of the growth of the crop and decrease the yield ofan agriculture field.

The term ‘natural environment’ may—in the context of plants like crop orweed—denote that the plants grow in a field or on land which may beexposed to natural weather and environmental conditions, like humidityand direct or indirect sun light. Hence, natural environment excludesartificial environments like glass houses or other non-natural growingenvironments for plants. Such unnatural environments with controlledconditions may artificially exclude numerous influence factors, whichmake the recognition process much harder or—in many cases—impossible.This feature may prove to be an advantage of the currently proposedmethod and system because it may be much easier to differentiate betweencrop and weed in an artificially controlled environment. Under extremelycontrolled conditions it may be much easier to recognize a specific typeof weed because a large number of types of weeds may be excluded upfrontgiven the specific and controlled conditions of, e.g., a glass house.

The term ‘capturing an image’ may denote the classical process of makinga photograph, however, in digital form. For capturing a digital image,some well-known optical elements together with an image sensor may berequired, as can be found in typical digital newcomers these days. Suchdigital cameras have become an often implemented feature in asmartphone. The term ‘capturing an image’ may denote the process oftaking a digital picture.

The term ‘early development stage’ may denote a stage of a plant inwhich the plant, in particular the weed, may not have grown to an adultstage. Very early development stages may be difficult to recognizeanyway. It has been shown that the usage of the ‘BBCH code’ may beuseful when describing development stages of plants, e.g., weed. Theabbreviation BBCH stands officially for “Biologische Bundesanstalt,Bundessortenamt and Chemische Industrie” and describes phenologicaldevelopment stages of a plant. The code goes from 00 to 99. A BBCH codeof 10 to 19 represents different early development stadiums of leafs.The principal growth stage 2 includes BBCH codes 20 to 29 and is aboutformation of side shoots/tillering. The principal growth stage 3 (BBCHcodes 30 to 39) comprises stem elongation/shoot development (mainshoot). Thus, focusing on weed with BBCH codes between 10 and 39 mayrepresent a good focus on weed in an early development stage.

The term ‘contouring’ may denote a process of a determination of acontour of a certain area or surface having common color and/or textualcharacteristics of, e.g., weed in a digital picture. Each leaf ofplants, in particular weed, has a natural boundary or outer edge r edgesof the leaf. The process of contouring captures, recognizes ordetermines these edges such that inside the related contoured area allor almost all pixels of the weed may be included.

The term ‘color and texture specification’ may denote digitalinformation about pixels in a digital image according to a color model,e.g., the RGB color model (red, green, blue). However, other colormodels may be used, like HSV (hue, saturation, value), HSL (hue,saturation, lightness/luminance). It is well known in the industry thatmost color model information from one color model may be transformed toanother color model by a mathematical matrix operation. Different colormodels may have different advantages like most natural colorrepresentations, best suitable for digital processing, optimally adaptedfor grayscale processing, best suited for edge recognition and thus forcontouring, and so on.

The term ‘RGB color model’ may denote the well-known additive colormodel in which red, green, and blue lights are added together in variousways to reproduce a broad array of colors. The name of the model comesfrom the initials of the three additive primary colors, red, green, andblue. The main purpose of the RGB color model is for the sensing,representation, and display of images in electronic systems, such astelevisions and computers, though it has also been used in conventionalphotography. Before the electronic age, the RGB color model already hada solid theory behind it, based in human perception of colors. RGB is adevice-dependent color model: different devices may detect or reproducea given RGB value differently since the color elements (such asphosphors or dyes) and their response to the individual R, G, and Blevels vary from manufacturer to manufacturer, or even in the samedevice over time. Thus, an RGB value may not define the same coloracross devices without some kind of color management and mathematicaltransformations.

The term ‘convex frame’ may denote a closed frame surrounding an objectcaptured as part of a digital image. The frame may comprise individualconnected lines like a closed polygon. The term ‘convex’ may denote thatthere are no concave sub-portions as element of the convex frame. Thus,each two sets of connecting lines may never have an inner angle—i.e.,oriented to the inner side of the frame—larger than 180°. It may beadvantageous to use a regular or symmetrical frame, like a triangle, asquare, a rectangle, a pentagon, a hexagon, a parallelogram, and so on.

The term ‘focus detector’ may denote a device adapted for determiningwhether a captured digital image is sharp in an optical sense. Thismeans that the picture plane and the projection plane are selectedaccording to optical laws of the used lens or lenses.

The term ‘quality criterion’ may denote a criterion that may be relatedto a quality of a digital image, in particular of the captured weed. Itmay, e.g., require that a smallest of a predefined

convex frame requires a minimum amount of the total available size ofthe digital image, e.g., 10% or more (e.g., 90%, other values are alsopossible; typically 30 to 50%), and that a predefined minimum sharpnessof the captured area relating to the weed may be achieved. The firstrequirement (smallest of a predefined convex frame) may ensure thatenough digital picture information is available related to the weed; andthe second criterion (sharpness) may ensure that a minimum quality ofthe digital image is guaranteed. A recognition of weed from non-sharpdigital images may be extremely difficult or impossible.

The term ‘classifier correlation function’, and in particular ‘trainedclassifier correlation function’ may denote one or more mathematicalfunction allowing to measure a similarity of features between one ormore sections of a captured image and a pre-trained set of referenceimage data. The feature parametrization of a correlation function maynot be programmed manually but may be trained, i.e., learned usingdatasets with a known plurality of input attributes as well as thedesired result. A skill person will know various types of correlationapproaches.

Actually, this approach is also used for the texture specification. Nodirect parameters are specified but automatically derived during thetraining sessions of the classifier correlation function(s).

In the following, a detailed description of the figures will be given.All instructions in the figures are schematic. Firstly, a block diagramof an embodiment of the inventive method for recognizing of a type ofweed in a natural environment is given. Afterwards, further embodimentsas well as embodiments of the recognition system for a recognition of atype of weed in a natural environment will be described.

FIG. 1 shows a block diagram of an embodiment of the method 100 forrecognizing a type of weed in a natural environment. The methodcomprises a plurality of steps, namely: capturing, 102, a digitalimage—in particular a colored image—of a weed among cultivated crop inthe natural environment, which should explicitly not be an artificial orgreen house environment. The weed is in an early development stage. Themethod comprises further contouring, 104, areas with a predefined colorand texture specification in an RGB color model within the digitalimage. Typically, one may expect one contoured area from one weed plant.However, there may also be more than one contoured area from different,potentially not connected leafs, from two weed plants, or the like.—Sucha detection or determining process detects boundaries of green areas ofthe digital image. During this process at least one contoured area—e.g.,one or more leafs, as well as one or more weed plants—may be builtcomprising pixels relating to the weed within a boundary contour.However, it may also be possible, that the digital image has capturedmore than one leaf and/or the stem. Consequently, more than onecontoured area may be determined.

Next, the captured digital image is displayed, 106, together with asmallest of a predefined convex frame. Such a frame may preferably be arectangular frame. However, also other frame shapes may be allowed likea circle, a parallelogram, a triangle, a pentagon, a hexagon, and so on.For practical reasons, the convex frame may have a symmetrical form,which may be advantageous during calculation processes. A display of thesmartphone may be used for this.

Together with the digital image and the smallest of the predefinedconvex frame—in particular a predefined convex frame shape—an indicatorvalue is displayed, 108. The indicator value is indicating of apredefined quality criterion of the digital image. This may be based ondetermining that the smallest of the predefined convex frame covers apredefined minimum area—e.g., 10% to 90%, typically 50%—of an availabledisplay area. This may ensure a minimum size of the digital image incomparison to the maximum display or image size. As can be understood,the higher the percentage is the better is the resolution of thecaptured weed image. Additionally, the indicator value is based on ameasurement of a positive output value of a focus detector in respect tothe contoured areas. This may ensure a minimum required sharpness of thecaptured weed leaf. It may be understood, that several techniques forensuring optical sharpness of the captured weed may be applied, e.g.,histogram functions working on single intensity values of a specificcolor model. This implies that the positive output value is related toan image sharpness of an area of the digital image relating to thecontoured area, and thus, to a leaf or a group of leafs. The indicatormay be displayed in form of a color coding—in particular traffic lightcolors—of the smallest frame surrounding the captured area, i.e., theleaf(s) of the weed. However, also numerical value, a slider indicatoror other displayed of sound indicator types may be applicable.

The method further comprises storing, 110, the digital image only if theindicator value indicates that the digital image meets the predefinedquality criterion. Next, color information of the digital image outsidethe redefined convex frame is reset, 112, before the digital image maybe sent, 116—in particular to a server, e.g., for further examination.Based on that further examination, a weed name or a plurality of weednames of the weed of the captured image and a related probability valuemay be received, 118. The probability value may be indicative of aprobability of a match between the determined weed name and the weed ofthe captured digital image. Additionally, also a typical picture of theweed may be received and displayed together with the name and theprobability value for the correctness of the recognition. For thedetermination of the weed name and the probability value cognitivecomputing technologies may be applied. This may include one or moreneural network systems.

FIG. 2 shows a plant, in particular weed and an image capturing device202. This may be a camera of a smartphone (not shown). The camera mayhave an objective and a certain opening angle 204. The captured digitalimage may capture the leaf(s) 204 of the weed, here a dicotyledon. Thestem 208 may, e.g., be covered by the one or more leafs 206. In thecrosssection also the earth surface 210 and the root 212 of the weed isshown. In an optimal case, the camera's image plane 202 may becompletely parallel to a longitudinal extension of the leafs 206, i.e.,along the earth surface. However, smaller deviations indicated by theangle α may be acceptable. If the angle α may exceed a predefinedthreshold value, it may be indicated by the above-mentioned indicator.FIG. 2 is directed towards dicotyledon. The indicator may alsoanticipate that the leafs of the weed may not grow parallel to the soilsurface. Thus, in special cases, other tilt angles than 10° may beallowable. A blocking of a capturing the image, e.g., by a red flame,may be overwritten by a user. It may be assumed that the image detector203 is vertical to the objective of the camera 202.

It may also be noted that the digital image may not only have capturedmore than one leaf of a weed, but also more than a plant of potentialweed. However, ideally, each captured image would only have one weedplant or leaf for an easier recognition process.

FIG. 3 shows a comparable scenario for monocotyledon, e.g. grass.Capturing a digital image from above may not reveal enough informationfor a classification and determination of the type of weed. Therefore,monocotyledon may be digitally photographed from the side of the plant,or the weed may be extracted from the soil and put flat on the soil. Inthat case, a capturing of the weed may be performed as discussed in thecase of FIG. 2. Otherwise, the optical capturing plane 203 of thedigital camera 202 should be vertical to the earth surface. Also here,smaller deviation indicated by the angle α may be acceptable. If theangle α may exceed a predefined threshold value, it may be indicated bythe above-mentioned indicator.

FIG. 4a, 4b, 4c show the process of contouring. FIG. 4a shows aprojection of a weed leaf 402 on an image sensor 203 and/or a respectivematrix of pixels. Each small square 403 may represent one pixel. Askilled person will acknowledge that for the purpose of explanation avery coarse-grained matrix is used here. Typically, the camera may havea resolution of several million pixels. FIG. 4b shows the result of adigitization, 404. Now, the smooth edges of the leaf 402 are gone andthe originally analogue shape is digitized. In a next step—FIG. 4c —thecontour of the potential leaf may be extracted from the digital image.Everything inside the contour 406 may be counted as a leaf pixel.

FIG. 5a shows details of the determination process whether a certainpixel may belong to a weed leaf or not. In the simplest case, the colorvalue of a simple pixel p_(i) 502 is used as determination criterion.The related color range may comprise, e.g., wavelengths of 495 to 575nm. However, sometimes such a simple differentiation may not besufficient. Therefore, pixels surrounding the pixel 502 in question arealso taken into account. FIG. 5 shows a first “ring” of additionalpixels directly surrounding pixel 502. Equation (1) may be used to alsouse the information related to pixel p_(i) 502.

FIG. 5b shows in addition to the first “ring” 504 of pixels surroundingpixel 502 a second “ring” 506 of surrounding pixels. The influence orweighing of the decision whether pixel 502 is determined to be a leafpixel or not, may decrease the further away a certain pixel may be fromthe pixel 502 in question. Further rings of pixels p_(i,j) may be usedand may become variables of the function F. The number of rings used maybe a function of the total resolution of the image sensor.

FIG. 6 shows a digital image 502 of weed comprising two leafs on adisplay 604. A smallest convex frame is shown in form of a dotted linerectangular frame 606. The color of this rectangular frame 606 may beused as indicator value. E.g., a red colored frame may indicate that thequality of the digital image of weed 602 may not be sufficient. If thequality of digital image of the weed 602 increases, the color of theframe 606 may turn to yellow. If the quality is sufficient regarding thequality criterion for a further processing and a recognition process ofthe weed, the frame's 606 color may be converted to green. However,other color values may be used. Additionally, or as an alternative,there may also be a display of a numeric value on the screen 600 for atraffic light symbol or any other easy to interpret indicators.Additionally, the determined quality of the digital image of the weed602 may also be indicated by sound. This could be speech synthesis orany other acoustic signal combination.

FIG. 7 shows a block diagram of an embodiment of the recognition system700 for recognition of a type of weed in a natural environment. Therecognition system comprises a digital camera 702 adapted for capturinga digital image of a weed among cultivated crop in the naturalenvironment. The weed should be in an early development stage. Acontouring module 704 is adapted for contouring areas with a predefinedcolor and texture specification in an RGB color model within the digitalimage building at least one contoured area comprising pixels relating tothe weed within a boundary.

A display 706 is adapted for displaying the digital image together witha smallest of a predefined convex frame surrounding the contoured area,in which the display 706 is also adapted for displaying an indicatorvalue together with the digital image and the smallest of the predefinedconvex frame. The indicator is indicating of a predefined qualitycriterion of the digital image. This may be based on a determinationthat the smallest of the predefined convex frame covers a predefinedminimum area of an available display area, and a measurement of apositive output value of a focus detector in respect to the contouredarea, wherein the positive output value is related to an image sharpnessof an area of the digital image relating to the contoured area. Thedisplay 706 may be the, e.g., touch-sensible, display of a smartphone.

The digital image may then be stored into storage 708 only if theindicator value indicates that the digital image meets the predefinedquality criterion. A resetting unit 710 is adapted for resetting colorinformation of the digital image outside the redefined convex frame, atransmission unit or sender 712 is adapted for sending the digital imagefor a further examination, and a receiver 714 is adapted for receiving aweed name of the weed of the captured image and a related probabilityvalue indicative of a probability of a match between the weed name andthe weed of the captured digital image. This way, a farmer—even anexperienced one—may be able to differentiate between different types ofweed on his field among crop.

Embodiments of the invention may be implemented together with virtuallyany type of computer—in particular a smartphone—regardless of theplatform being suitable for storing and/or executing program code. FIG.8 shows, as an example, a computing system 800 suitable for executingprogram code related to the proposed method. Beside smartphones alsoother mobile devices with a camera, a processor for executing programcode and a transceiver may be suited for the implementation of theproposed method and/or related recognition system.

The computing system 800 is only one example of a suitable computersystem and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, computer system 800 is capable of being implemented and/orperforming any of the functionality set forth hereinabove. In thecomputer system 800, there are components, which are operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 800 include, but are not limited to, tabletcomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, programmable consumer electronics,smartphones, and digital camera with spare computing capacity thatinclude any of the above systems or devices, and the like. Computersystem 800 may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system 800. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes.

As shown in the figure, computer system 800 is shown in the form of ageneral-purpose computing device. The components of computer system 800may include, but are not limited to, one or more processors orprocessing units 802, a system memory 804, and a bus 818 that couplesvarious system components including system memory 804 to the processor802. Computer system 800 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 800, and it includes both, volatile and non-volatilemedia, removable and non-removable media.

The system memory 804 may include computer system readable media in theform of volatile memory, such as random access memory (RAM) and/or cachememory. Computer system 800 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 812 may be provided forreading from and writing to a non-removable storage chip. Storage mediacan be connected to bus 806 by one or more data media interfaces. Aswill be further depicted and described below, memory 804 may include atleast one program product having a set (e.g., at least one) of programmodules that are configured to carry out the functions of embodiments ofthe invention.

A program/utility, having a set (at least one) of program modules, maybe stored in memory 804 by way of example, and not limitation, as wellas an operating system, one or more application programs, other programmodules, and program data. Program modules may generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

The computer system 800 may also communicate with one or more externaldevices such as a keyboard, a pointing device, a display 820, etc.;these devices may be combines in a touch-screen that enable a user tointeract with computer system 800; and/or any devices (e.g., networkcard, modem, etc.) that enable computer system 800 to communicate withone or more other computing devices. Such communication can occur viainput/output (I/O) interfaces. Still yet, computer system 800 maycommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a mobile public network(e.g., the Internet) via network adapter 822. As depicted, networkadapter 814 may communicate with the other components of computer system800 via bus 818. It should be understood that although not shown, otherhardware and/or software components could be used in conjunction withcomputer system 800. Examples, include, but are not limited to:microcode, device drivers, redundant processing units, etc.

Additionally, the recognition system 700 for recognition of a type ofweed in a natural environment may be attached to the bus system 818.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinaryskills in the art without departing from the scope and spirit of thedescribed embodiments. The terminology used herein was chosen to bestexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skills in the art to understand theembodiments disclosed herein.

The present invention may be embodied as a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention.

The medium may be an electronic, magnetic, optical, electromagnetic,infrared or a semiconductor system for a propagation medium, like e.g.,solid state memory, a random access memory (RAM), a read-only memory(ROM).

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device such as an EPROM, or any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to the respective computing devices, e.g. as a smartphone appfrom a service provider via a mobile network connection.

Computer readable program instructions for carrying out operations ofthe present invention may be any machine dependent or machineindependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as C++, Java or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the computerdevice. In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus', and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus', or anotherdevice to cause a series of operational steps to be performed on thecomputer, other programmable apparatus or other device to produce acomputer implemented process, such that the instructions which executeon the computer, other programmable apparatus', or another deviceimplement the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and/or block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or act or carry out combinations of special purpose hardwareand computer instructions.

The corresponding structures, materials, acts, and equivalents of allmeans or steps plus function elements in the claims below are intendedto include any structure, material, or act for performing the functionin combination with other claimed elements, as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skills in the artwithout departing from the scope and spirit of the invention. Theembodiments are chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skills in the art to understand the invention forvarious embodiments with various modifications, as are suited to theparticular use contemplated.

The invention claimed is:
 1. A method for recognizing a type of weed ina natural environment comprising: capturing a digital image of a weedamong cultivated crop in the natural environment, wherein the weed is inan early development stage, wherein the early development stage isdefined by a BBCH code from 10 to 39, contouring areas with a predefinedcolor and texture specification in an RGB color model within the digitalimage building at least one contoured area comprising pixels relating tothe weed within a boundary contour, sending the digital image for afurther examination, applying, to the digital image, a filter conversionof a digital greyscale image, which is derived from the digital image,wherein a standard deviation of greyscale intensities of pixels lyingwithin an area is masked by the contoured areas, and wherein a sharpnessfactor is derived as a squared standard deviation; and receiving, basedon the further examination, a weed name of the weed of the capturedimage and a related probability value indicative of a probability of amatch between the weed name and the weed of the captured digital image.2. The method according to claim 1, further comprising: displaying thedigital image together with a smallest of a predefined convex framesurrounding the contoured areas, displaying an indicator value togetherwith the digital image and the smallest of the predefined convex frame,wherein the indicator is indicating of a predefined quality criterion ofthe digital image based on determining that the smallest of thepredefined convex frame covers a predefined minimum area of an availabledisplay area, and measuring a positive output value of a focus detectorin respect to the contoured areas, wherein the positive output value isrelated to an image sharpness of an area of the digital image relatingto the contoured area, and storing the digital image only if theindicator value indicates that the digital image meets the predefinedquality criterion.
 3. The method according to claim 1, furthercomprising: resetting color information of the digital image outside apredefined convex frame, and/or resetting color information of areas ofthe digital image outside the related boundary contour.
 4. The methodaccording to claim 1, wherein the predefined color specification relatesto a color range of weed in a natural environment, in particular thegreen color range of wavelength 490 to 575 nm.
 5. The method accordingto claim 1, wherein the contouring areas with the predefined color andtexture specification is performed by determining for every pixel of thedigital image whether a combination of its color components matches oneof a plurality predefined color combinations.
 6. The method according toclaim 1, wherein the contouring of areas with the predefined colorspecification is performed additionally by a determination ofwi=F(pi,pi,j), wherein wi=1 or 0 indicating that pixel I belongs to weedor not, F is a function calculation a probability for weed/non weedbased on color attributes of pi and all of pj, pi=pixel i, pi,j=combinedpixels j surrounding the pixel i.
 7. The method according to claim 1,wherein the indicator is implemented as a color code of a predefinedconvex frame.
 8. The method according to claim 1, wherein the captureddisplayed image has a lower resolution than the digital image.
 9. Themethod according to claim 1, wherein the storing the digital imagecomprises also performing a step of capturing the same digital image ata higher resolution than the initially captured digital image,performing the step of contouring and a step of measuring a positiveoutput value of a focus detector using the digital image with the higherresolution.
 10. The method according to claim 1, wherein, in case theweed is a monocotyledon, the capturing the digital image is performedwith a digital image plane being parallel to a longitudinal expansion ofthe monocotyledon plus or minus a predefined first delta angle; and/orwherein, in case the weed is a dicotyledon, the capturing the digitalimage is performed with a digital image plane being parallel to thenatural environment surrounding the weed plus or minus a predefinedsecond delta angle and/or projecting the weed into the middle of animage capturing device.
 11. The method according to claim 1, wherein thefurther examination comprises applying at least one trained classifiercorrelation function comprising neural network classifiers and samplebased identifiers, both for weed and single leafs, to the captureddigital image for recognizing the weed, wherein the correlation functionhas access to names of types of weeds together with a plurality of setsof metadata per weed type.
 12. A method of weed control using the methodaccording to claim 1, wherein a name of an herbicide is selected out ofa database and is received together with the weed name and theprobability.
 13. A system for a recognition of a type of weed in anatural environment among cultivated crop, wherein the weed is in anearly development stage, the recognition system comprising a digitalcamera adapted for capturing a digital image of the weed in the naturalenvironment, a contouring module adapted for contouring areas with apredefined color and texture specification in an RGB color model withinthe digital image building at least one contoured area comprising pixelsrelating to the weed within a boundary, a sender adapted for sending thedigital image for a further examination, a filter adapted for applying,to the digital image, a filter conversion of a digital greyscale image,which is derived from the digital image, wherein a standard deviation ofgreyscale intensities of pixels lying within an area is masked by thecontoured areas, where wherein a sharpness factor is derived as asquared standard deviation; and a receiver adapted for receiving, basedon the further examination, a weed name of the weed of the capturedimage and a related probability value indicative of the probability of amatch between the weed name and the weed of the captured digital image.14. The system according to claim 13, further comprises: a displayadapted for displaying the digital image together with a smallest of apredefined convex frame surrounding the contoured areas, wherein thedisplay is also adapted for displaying an indicator value together withthe digital image and the smallest of the predefined convex frame,wherein the indicator is indicating of a predefined quality criterion ofthe digital image based on a determination that the smallest of thepredefined convex frame covers a predefined minimum area of an availabledisplay area, and a measurement of a positive output value of a focusdetector in respect to the contoured area, wherein the positive outputvalue is related to an image sharpness of an area of the digital imagerelating to the contoured area, and a storage adapted for storing thedigital image only if the indicator value indicates that the digitalimage meets the predefined quality criterion.
 15. The system accordingto claim 13, further comprises: a resetting unit adapted for resettingcolor information of the digital image outside a predefined convexframe, and/or resetting color information of areas of the digital imageoutside the related boundary contour.
 16. A non-transitorycomputer-readable medium having stored thereon a computer programproduct for recognizing of a type of weed in a natural environment amongcultivated crop, wherein the weed is in an early development stage,wherein the early development stage is defined by a BBCH code from 10 to39, said computer program product comprising program instructionsembodied therewith, said program instructions being executable by one ormore computing devices to cause said one or more computing devices tocapture a digital image of the weed in the natural environment, contourareas with a predefined color and texture specification in an RGB colormodel within the digital image building at least one contoured areacomprising pixels relating to the weed within a boundary contour,display the digital image together with a smallest of a predefinedconvex frame surrounding the contoured areas, display an indicator valuetogether with the digital image and the smallest of the predefinedconvex frame, wherein the indicator is indicating of a predefinedquality criterion of the digital image based on send the digital imagefor a further examination, apply, to the digital image, a filterconversion of a digital greyscale image, which is derived from thedigital image, wherein a standard deviation of greyscale intensities ofpixels lying within an area is masked by the contoured areas, andwherein a sharpness factor is derived as a squared standard deviation;and receive, based on the further examination, a weed name of the weedof the captured image and a related probability value indicative of aprobability of a match between the weed name and the weed of thecaptured digital image.
 17. The computer program according to claim 16,wherein said program instructions being executable by one or morecomputing devices to cause said one or more computing devices todisplaying the digital image together with a smallest of a predefinedconvex frame surrounding the contoured areas, displaying an indicatorvalue together with the digital image and the smallest of the predefinedconvex frame, wherein the indicator is indicating of a predefinedquality criterion of the digital image based on determining that thesmallest of the predefined convex frame covers a predefined minimum areaof an available display area, and measuring a positive output value of afocus detector in respect to the contoured areas, wherein the positiveoutput value is related to an image sharpness of an area of the digitalimage relating to the contoured area, and storing the digital image onlyif the indicator value indicates that the digital image meets thepredefined quality criterion.
 18. The computer program according toclaim 16, wherein said program instructions being executable by one ormore computing devices to cause said one or more computing devices toresetting color information of the digital said image outside thepredefined convex frame, and/or resetting color information of areas ofthe digital image outside the related boundary contour.